First session:

• Not heaps of paleo-specific R
• But building blocks to make you an expeRt
• Things that go into R (data inputs)
• How to structure your data inputs and outputs
• Getting started in R

Second session:

• Data validation
• Data visualisation
• NMDS
• RDA
• Plotting NMDS etc for publications
• Saving and export

1. Raw data (as entered)
2. Corrected and modified data

Keep a record of how you went from (1) to (2) - even if you don't do it in R

1. Correct/modify data in R (with reminders)
2. Create a new spreadsheet and keep a .txt records

Pros and cons of the following:

• MW-LCR shared drives
• Dropbox (C:/)
• Github (C:/)
• MW-LCR personal drive

http://www2.stat.duke.edu/~rcs46/lectures_2015/01-markdown-git/slides/naming-slides/naming-slides.pdf

Three principals:

1. Machine readable
2. Human readable
3. Works with default ordering

• no punctuation symbols
• no spaces
• be careful with capitals
• for data, easy to parse

• e.g. year_site_coreNUM_type
• e.g. 2018-06-31_eweburn_X18-062_concentrations.csv
• e.g. 2018-06-31_eweburn_X18-062_age-depth.csv
• e.g. 2018-06-31_eweburn_X18-062_species-dictionary.csv

note that we separate units of metadata with a "_" and within units, with a "-".

• This applies to scripts and data

• e.g. 1_data-cleaning-vegetation.R
• e.g. 2_data-cleaning-species-dictionary.R
• e.g. function_plot-all-species.R

• e.g. function_clean-italics-tilia.R

getwd()

## [1] "/Users/oliviaburge/Documents/paleo-R-workshop/1-folders-spreadsheets-organisingData"

setwd()

sessionInfo()

## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.5
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_NZ.UTF-8/en_NZ.UTF-8/en_NZ.UTF-8/C/en_NZ.UTF-8/en_NZ.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] DiagrammeR_1.0.0
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.17       highr_0.6          pillar_1.2.1      
##  [4] compiler_3.5.0     RColorBrewer_1.1-2 influenceR_0.1.0  
##  [7] plyr_1.8.4         bindr_0.1.1        viridis_0.5.1     
## [10] tools_3.5.0        digest_0.6.15      jsonlite_1.5      
## [13] viridisLite_0.3.0  gtable_0.2.0       evaluate_0.10.1   
## [16] tibble_1.4.2       rgexf_0.15.3       pkgconfig_2.0.1   
## [19] rlang_0.2.1        igraph_1.2.1       rstudioapi_0.7    
## [22] yaml_2.1.19        bindrcpp_0.2.2     gridExtra_2.3     
## [25] downloader_0.4     dplyr_0.7.5        stringr_1.3.1     
## [28] knitr_1.20         htmlwidgets_1.2    hms_0.4.2         
## [31] grid_3.5.0         rprojroot_1.3-2    tidyselect_0.2.4  
## [34] glue_1.2.0.9000    R6_2.2.2           Rook_1.1-1        
## [37] XML_3.98-1.11      rmarkdown_1.10     ggplot2_2.2.1.9000
## [40] tidyr_0.8.0        purrr_0.2.5        readr_1.1.1       
## [43] magrittr_1.5       backports_1.1.2    scales_0.5.0      
## [46] htmltools_0.3.6    assertthat_0.2.0   colorspace_1.3-2  
## [49] brew_1.0-6         stringi_1.2.3      visNetwork_2.0.3  
## [52] lazyeval_0.2.1     munsell_0.4.3

require(tidyverse)

## Loading required package: tidyverse

## ── Attaching packages ────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 2.2.1.9000     ✔ purrr   0.2.5     
## ✔ tibble  1.4.2          ✔ dplyr   0.7.5     
## ✔ tidyr   0.8.0          ✔ stringr 1.3.1     
## ✔ readr   1.1.1          ✔ forcats 0.3.0

## ── Conflicts ───────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

# install.packages("vegan")
# install.packages("skimr")
require(vegan)
require(skimr)
data("mite")             # the data command only works for in-built data
data("mite.env")         # we'll cover reading in your own data later on

head(mite, n = 6)

skim(mite)

## Skim summary statistics
##  n obs: 70 
##  n variables: 35 
## 
## ── Variable type:integer ──────────────────────────────────────────────────────────────────
##  variable missing complete  n  mean    sd p0  p25  p50   p75 p100     hist
##    Brachy       0       70 70  8.73 10.08  0 3     4.5 11.75   42 ▇▂▁▂▁▁▁▁
##  Ceratoz1       0       70 70  1.29  1.46  0 0     1    2       5 ▇▆▁▃▁▁▁▁
##  Ceratoz3       0       70 70  1.3   2.2   0 0     0    2       9 ▇▁▁▁▁▁▁▁
##  Eupelops       0       70 70  0.64  0.99  0 0     0    1       4 ▇▃▁▁▁▁▁▁
##      FSET       0       70 70  1.86  3.18  0 0     0    2      12 ▇▂▁▁▁▁▁▁
##  Galumna1       0       70 70  0.96  1.73  0 0     0    1       8 ▇▁▁▁▁▁▁▁
##      HMIN       0       70 70  4.91  8.47  0 0     0    4.75   36 ▇▁▁▁▁▁▁▁
##     HMIN2       0       70 70  1.96  3.92  0 0     0    2.75   20 ▇▂▁▁▁▁▁▁
##      HPAV       0       70 70  8.51  7.56  0 4     6.5 12      37 ▇▇▃▃▁▁▁▁
##      HRUF       0       70 70  0.23  0.62  0 0     0    0       3 ▇▁▁▁▁▁▁▁
##      LCIL       0       70 70 35.26 88.85  0 1.25 13   44     723 ▇▁▁▁▁▁▁▁
##  Lepidzts       0       70 70  0.17  0.54  0 0     0    0       3 ▇▁▁▁▁▁▁▁
##      LRUG       0       70 70 10.43 12.66  0 0     4.5 17.75   57 ▇▂▂▁▁▁▁▁
##      MEGR       0       70 70  2.19  3.62  0 0     1    3      17 ▇▂▁▁▁▁▁▁
##  Miniglmn       0       70 70  0.24  0.79  0 0     0    0       5 ▇▁▁▁▁▁▁▁
##      MPRO       0       70 70  0.16  0.47  0 0     0    0       2 ▇▁▁▁▁▁▁▁
##      NCOR       0       70 70  1.13  1.65  0 0     0.5  1.75    7 ▇▃▂▂▁▁▁▁
##      NPRA       0       70 70  1.89  2.37  0 0     1    2.75   10 ▇▂▂▁▁▁▁▁
##      ONOV       0       70 70 17.27 18.05  0 5    10.5 24.25   73 ▇▃▂▁▁▁▁▁
##  Oppiminu       0       70 70  1.11  1.84  0 0     0    1.75    9 ▇▁▁▁▁▁▁▁
##  Oribatl1       0       70 70  1.89  3.43  0 0     0    2.75   17 ▇▁▁▁▁▁▁▁
##      PHTH       0       70 70  1.27  2.17  0 0     0    2       8 ▇▁▁▁▁▁▁▁
##     PLAG2       0       70 70  0.8   1.79  0 0     0    1       9 ▇▁▁▁▁▁▁▁
##      PPEL       0       70 70  0.17  0.54  0 0     0    0       3 ▇▁▁▁▁▁▁▁
##   Protopl       0       70 70  0.37  1.61  0 0     0    0      13 ▇▁▁▁▁▁▁▁
##      PWIL       0       70 70  1.09  1.71  0 0     0    1       8 ▇▁▁▁▁▁▁▁
##      RARD       0       70 70  1.21  2.78  0 0     0    1      13 ▇▂▁▁▁▁▁▁
##      SLAT       0       70 70  0.4   1.23  0 0     0    0       8 ▇▁▁▁▁▁▁▁
##      SSTR       0       70 70  0.31  0.97  0 0     0    0       6 ▇▁▁▁▁▁▁▁
##  Stgncrs2       0       70 70  0.73  1.83  0 0     0    0       9 ▇▁▁▁▁▁▁▁
##      SUCT       0       70 70 16.96 13.89  0 7.25 13.5 24      63 ▇▇▆▅▂▁▁▁
##  Trhypch1       0       70 70  2.61  6.14  0 0     0    2      29 ▇▁▁▁▁▁▁▁
##  Trimalc2       0       70 70  2.07  5.79  0 0     0    0      33 ▇▁▁▁▁▁▁▁
##      TVEL       0       70 70  9.06 10.93  0 0     3   19      42 ▇▁▁▂▁▁▁▁
##      TVIE       0       70 70  0.83  1.47  0 0     0    1       7 ▇▁▁▁▁▁▁▁

• Select some columns
• Filter just some observations
• Histogram of one column

Select chooses certain columns - to keep, or to get rid of. The format is

DATANAME %>% select(col1, col2, col3)

mite %>% 
  select(Brachy, PHTH, HPAV)

##    Brachy PHTH HPAV
## 1      17    5    5
## 2       2    7   16
## 3       4    3    1
## 4      23    7   10
## 5       5    8   13
## 6      19    7    5
## 7      17    3    8
## 8       5    4    8
## 9       3    3    2
## 10     22    4    5
## 11     36    7   35
## 12     28    2   12
## 13      3    2    4
## 14     41    5   12
## 15      6    0    6
## 16      7    2    3
## 17      9    0    1
## 18     19    3    7
## 19     12    2   10
## 20      3    1    7
## 21      5    2    8
## 22      4    0    4
## 23     19    0    8
## 24      4    0    1
## 25     12    4   15
## 26      6    0    4
## 27      4    4    4
## 28      9    0    4
## 29     42    0    6
## 30     20    1    2
## 31     12    0    5
## 32      4    0    9
## 33     38    0   17
## 34      5    0   14
## 35      3    0    0
## 36      3    1    2
## 37      3    0    5
## 38      8    0    6
## 39      0    0    0
## 40      1    0   31
## 41      2    0   10
## 42      0    0   12
## 43      5    0    2
## 44      0    0    2
## 45     11    0    8
## 46      4    0    4
## 47      0    0    8
## 48      0    0    3
## 49     10    0   14
## 50      4    0   37
## 51      2    0    5
## 52      3    0    4
## 53      3    0   17
## 54      2    0    7
## 55      1    0    3
## 56      1    0   16
## 57      0    0    0
## 58      0    0   12
## 59      1    0    0
## 60      1    0   16
## 61      6    0    9
## 62      3    0    5
## 63     19    0    3
## 64      3    0   16
## 65      4    0   10
## 66      8    0   18
## 67      4    0    3
## 68      6    0   22
## 69     20    2    4
## 70      5    0   11

names(mite)

##  [1] "Brachy"   "PHTH"     "HPAV"     "RARD"     "SSTR"     "Protopl" 
##  [7] "MEGR"     "MPRO"     "TVIE"     "HMIN"     "HMIN2"    "NPRA"    
## [13] "TVEL"     "ONOV"     "SUCT"     "LCIL"     "Oribatl1" "Ceratoz1"
## [19] "PWIL"     "Galumna1" "Stgncrs2" "HRUF"     "Trhypch1" "PPEL"    
## [25] "NCOR"     "SLAT"     "FSET"     "Lepidzts" "Eupelops" "Miniglmn"
## [31] "LRUG"     "PLAG2"    "Ceratoz3" "Oppiminu" "Trimalc2"

mite %>% 
  select(-c(PHTH:Oppiminu))

##    Brachy Trimalc2
## 1      17        0
## 2       2        0
## 3       4        0
## 4      23        0
## 5       5        0
## 6      19        0
## 7      17        0
## 8       5        0
## 9       3        0
## 10     22        0
## 11     36        0
## 12     28        0
## 13      3        0
## 14     41        0
## 15      6        0
## 16      7        0
## 17      9        0
## 18     19        0
## 19     12        0
## 20      3        0
## 21      5        0
## 22      4        0
## 23     19        0
## 24      4        0
## 25     12        0
## 26      6        0
## 27      4        0
## 28      9        0
## 29     42        0
## 30     20        0
## 31     12        0
## 32      4        0
## 33     38        0
## 34      5        0
## 35      3        0
## 36      3        0
## 37      3        0
## 38      8        0
## 39      0        0
## 40      1        0
## 41      2        0
## 42      0        0
## 43      5        1
## 44      0        0
## 45     11        0
## 46      4        0
## 47      0        0
## 48      0        1
## 49     10        0
## 50      4        0
## 51      2        1
## 52      3        0
## 53      3        9
## 54      2        1
## 55      1        0
## 56      1        5
## 57      0        0
## 58      0        0
## 59      1        1
## 60      1        1
## 61      6        5
## 62      3        0
## 63     19        8
## 64      3       11
## 65      4       25
## 66      8        9
## 67      4       33
## 68      6       17
## 69     20        3
## 70      5       14

Chaining allows us to write code in the order we want it done. Otherwise, it must be wrapped in brackets with the first thing to be done right in the middle.

mite.env %>% select(Shrub, Topo)

means, take the mite.env dataframe, and then select the columns Shrub and Topo.

Filter selects rows in your dataframe, based on the conditions you specify. Same format as for select():

filter(DATA, CONDITION1)

• row only selected if condition satisfied

filter(DATA, CONDITION1 & CONDITION2)

• both conditions must be satisfied

filter(DATA, CONDITION1 | CONDITION2)

• one or both conditions must be satisfied

mite.env %>% filter(WatrCont > 650)

##   SubsDens WatrCont Substrate Shrub    Topo
## 1    64.75   691.79   Sphagn2   Few Blanket
## 2    62.38   708.16  Barepeat   Few Blanket
## 3    52.73   656.35   Sphagn1  None Blanket
## 4    52.12   826.96   Sphagn1  None Blanket

Here, Shrub has to equal "Few". If you want to select two values (such as two sites) see the next slide.

mite.env %>% filter(WatrCont > 650 & Shrub == "Few")

##   SubsDens WatrCont Substrate Shrub    Topo
## 1    64.75   691.79   Sphagn2   Few Blanket
## 2    62.38   708.16  Barepeat   Few Blanket

unique(mite.env$Substrate)

## [1] Sphagn1   Litter    Interface Sphagn3   Sphagn4   Sphagn2   Barepeat 
## Levels: Sphagn1 Sphagn2 Sphagn3 Sphagn4 Litter Barepeat Interface

mite.env %>% filter(Substrate %in% c("Litter", "Barepeat", "Interface"))

##    SubsDens WatrCont Substrate Shrub    Topo
## 1     54.99   434.81    Litter   Few Hummock
## 2     46.07   371.72 Interface   Few Hummock
## 3     80.59   266.78 Interface  Many Blanket
## 4     61.43   310.70    Litter  Many Blanket
## 5     37.25   239.51 Interface  Many Blanket
## 6     59.93   350.64 Interface  Many Blanket
## 7     35.41   321.87 Interface   Few Hummock
## 8     29.56   296.95 Interface  Many Hummock
## 9     44.10   383.83 Interface  Many Blanket
## 10    38.61   145.68 Interface  Many Hummock
## 11    32.27   291.59 Interface  Many Hummock
## 12    35.30   293.49 Interface  Many Blanket
## 13    32.86   323.12 Interface  Many Hummock
## 14    37.33   284.27 Interface  Many Blanket
## 15    53.17   367.11 Interface  Many Blanket
## 16    34.76   393.62 Interface   Few Blanket
## 17    47.74   528.44 Interface   Few Blanket
## 18    34.26   398.20 Interface   Few Blanket
## 19    26.60   386.37 Interface   Few Blanket
## 20    56.65   581.00 Interface   Few Blanket
## 21    62.38   708.16  Barepeat   Few Blanket
## 22    46.81   538.51 Interface   Few Blanket
## 23    33.98   323.96 Interface   Few Blanket
## 24    28.29   434.28 Interface  None Blanket
## 25    26.83   414.65 Interface  None Blanket
## 26    31.98   447.65 Interface  None Blanket
## 27    41.38   532.88 Interface  None Blanket
## 28    56.82   613.39  Barepeat  None Blanket
## 29    47.03   626.36 Interface  None Blanket
## 30    48.59   634.75 Interface  None Blanket
## 31    35.03   482.27 Interface  None Blanket

• We use the command ggplot which comes from the ggplot2 package.
• ggplot() just initiates the plot
• then we tell it to draw a histogram with the geom_histogram() part.

ggplot(data = DATAFRAME, aes(x = COLUMN_FOR_HISTOGRAM)) + geom_histogram()

ggplot(data = mite.env, aes(x = SubsDens)) +
  geom_histogram() 

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data = mite.env, aes(x = SubsDens)) +
  geom_histogram(binwidth = 10) 

Actually, it should be real, but it should also be tidy

• Where is the file
• How do we tell R to get there
  • This also depends where R has the working directory
• We need to tell R how to get from the working directory, to the file that we want to read in!

getwd()

setwd()

aMess <- read.csv("data/messyDataExample.csv")

head(aMess)

##   Ashburton.Lakes.weight.of.vegetation.harvest.subsample           X X.1
## 1                                                                       
## 2                                                  Date:                
## 3                                              Lab team:                
## 4                                                                       
## 5                                                                       
## 6                                                        Wet weight     
##            X.2          X.3          X.4                    X.5 X.6
## 1                                                                  
## 2                                                                  
## 3                                                                  
## 4                                                                  
## 5                                                                  
## 6 Wet weight 1 Wet weight 2 Wet weight 3 Average wet weight (g)    
##           X.7          X.8          X.9         X.10
## 1                                                   
## 2                                                   
## 3                                                   
## 4                                                   
## 5                                                   
## 6 Dry weight  Dry weight 1 Dry weight 2 Dry weight 3
##                     X.11 X.12 X.13 X.14 X.15 X.16 X.17 X.18 X.19 X.20 X.21
## 1                                    NA                                   
## 2                                    NA                                   
## 3                                    NA                                   
## 4                                    NA                                   
## 5                                    NA                                   
## 6 Average dry weight (g)             NA                                   
##   X.22 X.23 X.24 X.25 X.26 X.27 X.28 X.29 X.30 X.31 X.32
## 1             NA                                        
## 2             NA                                        
## 3             NA                                        
## 4             NA                                        
## 5             NA                                        
## 6             NA

• To see the whole thing: View(aMess)

• Compare the output of names(aMess) and names(mite.env)

[group task]

• Where is it
• Where is the working directory?
• Where is the file in relation to our working directory?

• What did we call it (ie the filename)?!

• Then we can read it back in

• Where is it?
• Where is the working directory?
• Where is the file in relation to our working directory?
• What is the filename?